Breast cancer is a disease with extensive histoclinical heterogeneity. Although conventional histological and clinical features have been correlated with prognosis, the same apparent prognostic type of breast tumors vary widely in their responsiveness to therapy and consequent survival of the patient. New prognostic and predictive markers are needed to accurately foretell a patient's response to drugs in the clinic. Such markers would facilitate the individualization of therapy for each patient.
The problem may be solved by the identification of new parameters that can better predict a patient's sensitivity to treatment or therapy. The classification of patient samples is a crucial aspect of cancer diagnosis and treatment. The association of a patient's response to drug treatment with molecular and genetic markers can open up new opportunities for drug development in non-responding patients, or distinguish a drug's indication among other treatment choices because of higher confidence in the efficacy. Further, the pre-selection of patients who are likely to respond well to a medicine, drug, or combination therapy may reduce the number of patients needed in a clinical study or accelerate the time needed to complete a clinical development program (M. Cockett et al., 2000, Current Opinion in Biotechnology, 11:602-609).
The major goal of pharmacogenomics research is to identify genetic markers that accurately predict a given patient's response to drugs in the clinic; such individualized genetic assessment would greatly facilitate personalized treatment. An approach of this nature is particularly needed in cancer treatment and therapy, where commonly used agents are ineffective in many patients, and side effects are frequent. The ability to predict drug sensitivity in patients is particularly challenging because drug responses reflect both the properties intrinsic to the target cells and also a host's metabolic properties. Efforts by those in the art to use genetic information to predict drug sensitivity have primarily focused on individual polynucleotides that have broad effects, such as the multidrug resistant polynucleotides, mdr1 and mrp1 (P. Sonneveld, 2000, J. Intern. Med., 247:521-534).
The development of microarray technologies for large scale characterization of polynucleotide expression pattern makes it possible to systematically search for multiple molecular markers and to categorize cancers into distinct subgroups that are not evident by traditional histopathological methods (J. Khan et al., 1998, Cancer Res., 58:5009-5013; A. A. Alizadeh et al., 2000, Nature, 403:503-511; M. Bittner et al., 2000, Nature, 406:536-540; J. Khan et al., 2001, Nature Medicine, 7(6):673-679; and T. R. Golub et al., 1999, Science, 286:531-537; U. Alon et al., 1999, Proc. Natl. Acad. Sci. USA, 96:6745-6750). Such technologies and molecular tools have made it possible to monitor the expression levels of a large number of transcripts within a cell at any given time (see, e.g., Schena et al., 1995, Science, 270:467-470; Lockhart et al., 1996, Nature Biotechnology, 14:1675-1680; Blanchard et al., 1996, Nature Biotechnology, 14:1649; and U.S. Pat. No. 5,569,588, issued Oct. 29, 1996 to Ashby et al.).
How differential polynucleotide expression is associated with health and disease is a basis of functional genomics, which is defined as the study of all of the polynucleotides expressed by a specific cell or a group of cells and the changes in their expression pattern during development, disease, or environmental exposure. Hybridization arrays, used to study polynucleotide expression, allow polynucleotide expression analysis on a genomic scale by permitting the examination of changes in expression of literally thousands of polynucleotides at one time. In general, for hybridization arrays, gene-specific sequences (probes) are immobilized on a solid state matrix. These sequences are then queried with labeled copies of nucleic acids from biological samples (targets). The underlying theory is that the greater the expression of a gene, the greater the amount of labeled target and thus, the greater output of signal. (W. M. Freeman et al., 2000, BioTechniques), 29:1042-1055).
Recent studies have demonstrated that polynucleotide expression information generated by microarray analysis of human tumors can predict clinical outcome (L. J. van't Veer et al., 2002, Nature, 415:530-536; M. West et al., 2001, Proc. Natl. Acad. Sci. USA, 98:11462-11467; T. Sorlie et al., 2001, Proc. Natl. Acad. Sci. USA, 98:10869-10874; M. Shipp et al., 2002, Nature Medicine, 8(1):68-74). These findings bring hope that cancer treatment will be vastly improved by better predicting the response of individual tumors to therapy.
Needed in the art are new and alternative methods and procedures to determine drug sensitivity in patients and which are necessary to treat diseases and disorders, particularly cancers such as breast cancer, based on patient response at a molecular level. By using cultured cells as a model of in vivo effects, the present invention advantageously focuses on cell-intrinsic properties that are exposed in cell culture and involves identified polynucleotides that correlate with drug sensitivity. The presently described discovery and identification of polynucleotides/marker polynucleotides (predictor polynucleotides and polynucleotide sets) in cell lines assayed in vitro can be used to correlate with drug responses in vivo, and thus can be extended to clinical situations in which the same polynucleotides are used to predict responses to drugs and/or chemotherapeutic agents by patients, with particular regard to breast cancer patients.